These last months, we have been exposed to all kinds of Data Visualizations, helping us to understand the impact of the COVID-19 pandemic. All kinds of charts, dashboards and infographics are telling a different story of the current pandemic.
In Belgium, the discussion on the reported numbers of Covid-19 cases has risen again: should it be a 7-day average or daily numbers? Next to that, questions are arising on the reliability and the freshness of the data.
More worryingly is the big amount of “Not applicable”-values. For almost 28% of the deaths the gender or age are unknown. For Flanders, this is the case for more than 50%. We know the reason for this, but are not able to tackle the root cause.
It is becoming clear that from a data maturity point of view, the central and local organisations were not ready for this pandemic.
It has also become clear that to be able to create a chart to compare the data from different countries, some obstacles have to be overcome. Not all countries share the same definition of a positive case, resulting in a wrong ranking of countries based on number of positive cases or mortality-rates.
Next to that, data is not always available or complete, as not every country or region is willing to share the data, or has built up the right data maturity in order to be able to collect and store the data.
An assessment of several pillars of data maturity (organisation, data availability, technology, … ) should be taken. The result of the scan could help in the creation of a strategic plan, in order to be able to overcome the issues, encountered during this pandemic.
Because in the end, these challenges should not stop us visualizing the data. We have to make sure the visualization is truthful, by for example clearly stating differences in definitions. If this makes the Visualization more complex, that shouldn’t be a hindrance.
As Alberto Cairo states in his book The Functional Art: An Introduction to Information Graphics and Visualization : “graphics should not simplify messages. They should clarify them, highlight trends, uncover patterns, and reveal realities not visible before.”
If you want to build chart on the Covid-19 data, then you can these points into consideration.
Author: Aagje Veys, DataViz & BI Consultant
Topic: Data Visualization